MLOps vs LLMOps: What Changes When the Model Is an LLM
MLOps operates machine-learning systems; LLMOps extends those practices to prompts, context, retrieval, providers, tools, evaluations and runtime behavior around large language models.
Evaluation Harness
Evaluation Harness
Build an evaluation harness to test quality and safety consistently across versions.
Articles
Managed Agent Harness vs Self-Hosted Agent Loop: What You Gain, What You Lose
“Self-hosted agent” can mean very different architectures. This guide separates the managed harness, self-hosted execution environment, and fully self-operated agent loop—and shows which control boundary teams actually need.
RAG Failed — But Which Layer Actually Failed? A Diagnostic Method
When a RAG answer is wrong, blaming retrieval or the model is too vague. This diagnostic method isolates source coverage, query construction, retrieval, ranking, context assembly, generation, evidence attribution, and freshness—so the actual failure can be reproduced and fixed.
Ollama Is Not the Product: Building Production-Ready Open-LLM Applications
Running a local model with Ollama is easy. Building a production-ready Open-LLM application is harder: it requires RAG, access control, provider abstraction, evaluation, logging, deployment discipline and a controlled application layer around the model.
Comprehensive Guide to Evaluation Harness: Mastering LLM Performance Evaluation
This guide provides a detailed walkthrough of Evaluation Harness, an essential framework for rigorously assessing large language model (LLM) capabilities in enterprise LLMOps pipelines. Learn setup, best practices, and advanced techniques to ensure reliable model benchmarking and optimization.